Multitasking via baseline control in recurrent neural networks

Abstract

Changes in behavioral state, such as arousal and movements, strongly affect neural activity in sensory areas, and can be modeled as long-range projections regulating the mean and variance of baseline input currents. What are the computational benefits of these baseline modulations? We investigate this question within a brain-inspired framework for reservoir computing, where we vary the quenched baseline inputs to a recurrent neural network with random couplings. We found that baseline modulations control the dynamical phase of the reservoir network, unlocking a vast repertoire of network phases. We uncovered a number of bistable phases exhibiting the simultaneous coexistence of fixed points and chaos, of two fixed points, and of weak and strong chaos. We identified several phenomena, including noise-driven enhancement of chaos and ergodicity breaking; neural hysteresis, whereby transitions across a phase boundary retain the memory of the preceding phase. In each bistable phase, the reservoir performs a different binary decision-making task. Fast switching between different tasks can be controlled by adjusting the baseline input mean and variance. Moreover, we found that the reservoir network achieves optimal memory performance at any first-order phase boundary. In summary, baseline control enables multitasking without any optimization of the network couplings, opening directions for brain-inspired artificial intelligence and providing an interpretation for the ubiquitously observed behavioral modulations of cortical activity.

Neural heterogeneity controls computations in spiking neural networks

“…Here, we extend this theory of inhibitory neurons’ role in neural computation to show how inhibitory gating could be tuned by increasing or decreasing the spike threshold heterogeneity of the inhibitory population. Such tuning of spike threshold heterogeneity could be achieved either developmentally or by heterogeneous action of neuromodulatory input ( 18 , 19 ).…”

“…In some instances, cells of a specific type have been demonstrated to have properties that vary along a continuum ( 16 , 17 ). In addition to heterogeneity of physiological properties, neurons within a population can express diverse receptors for a particular neuromodulator, causing that neuromodulator to have heterogeneous effects on local neural activity ( 18 , 19 ).…”